Engineering topics

Krajewski, Witold F.

Publications and source records attributed to Krajewski, Witold F..

9 records

DOE OSTI2022

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km 2 . Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km 2 ) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km 2 . The space-time scale, or reference time t r ) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for t r > 1.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Insights into storm direction effect on flood response

In this study, we investigate the directional influence of storm movement on catchment flood peak response using the synthetic circular basin. Due to the complexity in defining storm movements that require meteorological modeling, we adopt a novel approach of combining the basin rotation method (BRM) with a circular basin construct. A systematic basin rotation approach provides a proxy for the storm direction relative to the river network topology to study its hydrologic response. Using Stage-IV rainfall with 4-km by 1-hour resolution for 16 years period from 2003 to 2018 in Iowa, U.S., we systematically analyze consequent flood peak response due to storm directions (basin rotations) using a distributed hydrologic model called the Hillslope-Link Model (HLM). HLM has demonstrated the ability to reproduce observed streamflow for real river basins studied in Iowa. The hydrologic simulation results show that the BRM can quantify the relationship between the rainstorm direction and catchment hydrologic response. Also, the maximum flood peak response is strongly dependent on the interaction of the peak of the rainstorm with the peak of the width function. The results indicate significant differences in runoff volume and runoff peak and their interannual variability due to rainstorm direction. The study has important implications for flood frequency predictions and developing flood resilience and watershed management strategies, particularly due to changing storm tracks under a warming climate.

42 ENGINEERING↗

NASA NTRS1995

Uncertainty in vertically integrated liquid water content due to radar reflectivity observation error

Radar reflectivity is used to estimate meteorological quantities such as rainfall rate, liquid water content, and the related quantity, vertically integrated liquid (VIL) water content. The estimation of any of these quantities depends on several assumptions related to the characteristics of the physical processes controlling the occurrence and character of water in the atmosphere. Additionally, there are many sources of error associated with radar observations, such as those due to brighthand, hail, and drop size distribution approximations. This work addresses one error of interest, the radar reflectivity observation error; other error sources are assumed to be corrected or negligible. The result is a relationship between the uncertainty in VIL water content and radar reflectivity measurement error. An example application illustrates the estimation of VIL uncertainty from typical radar reflectivity observations and indicates that the coefficient of variation in VIL is much larger than the coefficient of variation in radar reflectivity.

French, Mark N.↗

NASA NTRS1994

Estimating rainfall in the tropics using the fractional time raining

The relationship between the fractional time raining and tropical rainfall amount is investigated using raingage data and a point process model of tropical rainfall. Both the strength and the nature of the relationship are dependent upon the resolution of the data used to estimate the fractional time raining. It is found that highly accurate estimates of rainfall amounts over periods of one month or greater can be obtained from the fractional time raining so long as high-time-resolution data are used. It is demonstrated that the relationship between the fractional time raining and monthly atoll rainfall is quasi-homogeneous within the monsoon trough region of the equatorial western Pacific.

Morrissey, Mark L.↗

NASA NTRS1991

On the estimation of climatological Z-R relationships

A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.

Krajewski, Witold F.↗

NASA NTRS1991

Optimal combining of ground-based sensors for the purpose of validating satellite-based rainfall estimates

Two problems related to radar rainfall estimation are described. The first part is a description of a preliminary data analysis for the purpose of statistical estimation of rainfall from multiple (radar and raingage) sensors. Raingage, radar, and joint radar-raingage estimation is described, and some results are given. Statistical parameters of rainfall spatial dependence are calculated and discussed in the context of optimal estimation. Quality control of radar data is also described. The second part describes radar scattering by ellipsoidal raindrops. An analytical solution is derived for the Rayleigh scattering regime. Single and volume scattering are presented. Comparison calculations with the known results for spheres and oblate spheroids are shown.

Krajewski, Witold F.↗

NASA NTRS1989

Nonlinear estimation of monsoon rainfall from radar and raingage data

Estimates of climatological rainfall are made as part of the validation program for the Tropical Rainfall Measuring Mission. Radar and raingage data collected during AMEX in Darwin, Australia during 1987 and 1988 are analyzed using a nonlinear merging methodology known as disjunctive cokriging (Azimi-Zonooz et al., 1989). The methodology is outlined and the results are used to determine the most advantageous time scale for estimation.

Krajewski, Witold F.↗